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Brain Sci. 2014, 4(1), 49-72; doi:10.3390/brainsci4010049

Toward a New Application of Real-Time Electrophysiology: Online Optimization of Cognitive Neurosciences Hypothesis Testing

1
Brain Dynamics and Cognition Team, Lyon Neuroscience Research Center, INSERM U1028-CNRS UMR5292, Lyon F-69000, France
2
University Lyon 1, Lyon F-69000, France
3
Brain and Spine Institute, Paris F-75000, France
*
Author to whom correspondence should be addressed.
Received: 1 November 2013 / Revised: 16 December 2013 / Accepted: 10 January 2014 / Published: 23 January 2014
(This article belongs to the Special Issue Emergence of Novel Brain-Computer Interface Applications)
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Abstract

Brain-computer interfaces (BCIs) mostly rely on electrophysiological brain signals. Methodological and technical progress has largely solved the challenge of processing these signals online. The main issue that remains, however, is the identification of a reliable mapping between electrophysiological measures and relevant states of mind. This is why BCIs are highly dependent upon advances in cognitive neuroscience and neuroimaging research. Recently, psychological theories became more biologically plausible, leading to more realistic generative models of psychophysiological observations. Such complex interpretations of empirical data call for efficient and robust computational approaches that can deal with statistical model comparison, such as approximate Bayesian inference schemes. Importantly, the latter enable the optimization of a model selection error rate with respect to experimental control variables, yielding maximally powerful designs. In this paper, we use a Bayesian decision theoretic approach to cast model comparison in an online adaptive design optimization procedure. We show how to maximize design efficiency for individual healthy subjects or patients. Using simulated data, we demonstrate the face- and construct-validity of this approach and illustrate its extension to electrophysiology and multiple hypothesis testing based on recent psychophysiological models of perception. Finally, we discuss its implications for basic neuroscience and BCI itself.
Keywords: brain-computer interfaces; real-time electrophysiology; adaptive design optimization; hypothesis testing; Bayesian model comparison; Bayesian Decision Theory; generative models of brain functions; cognitive neuroscience brain-computer interfaces; real-time electrophysiology; adaptive design optimization; hypothesis testing; Bayesian model comparison; Bayesian Decision Theory; generative models of brain functions; cognitive neuroscience
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Sanchez, G.; Daunizeau, J.; Maby, E.; Bertrand, O.; Bompas, A.; Mattout, J. Toward a New Application of Real-Time Electrophysiology: Online Optimization of Cognitive Neurosciences Hypothesis Testing. Brain Sci. 2014, 4, 49-72.

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